Robustness of NMF Algorithms Under Different Noises

In machine learning, datasets are often disturbed by different noises. The Nonnegative Matrix Factorization (NMF) algorithm provides a robust method to deal with noise, which will significantly improve the efficiency of learning. this investigation, standard NMF and L2,1-Norm Based studied designing experiments on noise types, levels, datasets. Furthermore, Relative Reconstruction Errors (RRE), accuracy, Normalized Mutual Information (NMI) used evaluate robustness two algorithms. experiment, there is no significant difference in performance between algorithms, while shows relatively small advantages.